Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning
Authors: Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Putting everything together, i Voro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, Tiny Image Net, and Image Net-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, i Voro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. |
| Researcher Affiliation | Academia | Chunwei Ma1, Zhanghexuan Ji1, Ziyun Huang2, Yan Shen1, Mingchen Gao1, Jinhui Xu1 1Department of Computer Science and Engineering, University at Buffalo 2Computer Science and Software Engineering, Penn State Erie 1EMAIL 2{zxh201}@psu.edu |
| Pseudocode | Yes | Algorithm 1: Voronoi Diagram-based Logistic Regression. Algorithm 2: i Voro Algorithm. Algorithm 3: i Voro-D Algorithm. |
| Open Source Code | Yes | Our code is available at https://machunwei.github.io/ivoro/. |
| Open Datasets | Yes | Three standard datasets, CIFAR-100 (Krizhevsky et al., 2009), Tiny Image Net (Le & Yang, 2015) and Image Net-Subset (Deng et al., 2009a) for CIL are used for method evaluation. |
| Dataset Splits | Yes | We follow the popular benchmarking protocol in exemplar-free CIL used by (Liu et al., 2021b; Zhu et al., 2021; Douillard et al., 2020; Hou et al., 2019) in which the inital phase contains a half of the classes while the subsequent phases each has 1 5, 1 10, or 1 20 of the remaining classes. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU/CPU models, memory details). |
| Software Dependencies | No | The paper does not explicitly list software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Specifically, for each phase Ī {1, ..., t}, the local dataset DĪ is used to train a logistic regression model (restricted by Thm. 2.1) with weight decay β at 0.0001 and initial learning rate at 0.001. |